a.Price Questions:
i. At What Price are the laptops actually selling ?
#### Histogram of the Retail Price of Computer In 2018
ggplot(Retail_Price_and_Dates) +
aes(x = Retail.Price) +
geom_histogram(bins = 30L, fill = "#1c6155") +
labs(x = "Price", y = "Frequency", title = "Histogram of the Retail Price of Computer", subtitle = "In 2018") +
theme_classic()

#### Boxplot of the Retail Price of Computer In 2018
ggplotly(
ggplot(Retail_Price_and_Dates) +
aes(x = "", y = Retail.Price) +
geom_boxplot(fill = "#1c6155") +
labs(y = "Price", x="",
title = "Boxplot of the Retail Price of Computer", subtitle = "In 2018") +
theme_classic()
)
print(paste("Last Recorded Prices are", Actual_Price[1,1], "USD", "and", Actual_Price[2,1],"USD","on the same Day with a mean of",mean(Actual_Price$Retail.Price),"USD"))
## [1] "Last Recorded Prices are 406 USD and 530 USD on the same Day with a mean of 468 USD"
ii.Does price change with time?
# Retail Price By Month
RetailPricePlot1 <- ggplotly(
ggplot(Retail_Price_and_Dates_Month) +
aes(x = Date, y = Mean_Retail_Price) +
geom_line(size = 1.1,
colour = "#1c6155") + geom_point() +
labs(x = "Months", y = "Average Retail Pric", title = "Average Retail Price of Computer in 2018",
subtitle = "Aggregated by Month") +
theme_classic()
)
RetailPricePlot1
# Retail Price By Week
RetailPricePlot2 <- ggplotly(
ggplot(Retail_Price_and_Dates_Week) +
aes(x = Date, y = Mean_Retail_Price) +
geom_line(size = 0.4,
colour = "#1c6155") + geom_point() +
labs(x = "Weeks", y = "Average Retail Pric", title = "Average Retail Price of Computer in 2018",
subtitle = "Aggregated by Week") +
theme_classic()
)
RetailPricePlot2
# Retail Price By Day
RetailPricePlot3 <- ggplotly(
ggplot(Retail_Price_and_Dates_Day) +
aes(x = Date, y = Mean_Retail_Price) +
geom_line(size = 0.2,
colour = "#1c6155") +
labs(x = "Days", y = "Average Retail Price", title = "Average Retail Price of Computer in 2018",
subtitle = "Aggregated by Day") +
theme_classic()
)
RetailPricePlot3
# Retail Price By Week Day
RetailPricePlot4 <- ggplotly(
ggplot(Retail_Price_and_Dates_WeekDay) +
aes(x = Weekday, y = Mean_Retail_Price) +
geom_col(fill = "#1c6155") +
labs(x = "Weekdays", y = "Average Retail Price", title = "Average Retail Price during Weekdays", subtitle = "In 2018") +
theme_classic() + coord_cartesian(ylim = c(505, 510))
)
RetailPricePlot4
iii. Are prices consistent across retail outlets?
#### Boxplot Across Retail Outlets
ggplotly(
ggplot(Retail_Price_Outlets_Date) +
aes(x = Store.Postcode, y = Retail.Price) +
geom_boxplot(fill = "#1c6155") +
labs(x = "Stores Postcode", y = "Price", title = "Boxplot Of The Retail Price Across Stores", subtitle = "In 2018") +
theme_classic() + scale_x_discrete(guide = guide_axis(n.dodge = 1)) + theme(axis.text.x=element_text(size=rel(1), angle=90))
)
#### Plot of the Monthly Retail Price per Stores
ggplot(Retail_Price_Outlets_Date_Month) +
aes(x = Floor.Date, y = Mean_Retail_Price, colour = Store.Postcode) +
geom_line(size = 0.5) +
scale_color_hue(direction = 1) +
labs(x = "Month", y = "Price", title = "Retail Price Across Months and Grouped by Stores",
subtitle = "In 2018") +
theme_classic()

iv. How does price change with configuration?
#### Plot Of The Retail Price per Configuration
ggplot(Retail_Price_Configuration) +
aes(x = Configuration, y = Retail.Price) +
geom_point(shape = "circle", size = 0.6) +
scale_color_gradient() +
labs(y = "Retail Price", title = "Retail Price and Configuration ",
subtitle = "In 2018") + geom_smooth() + theme_classic()

b.Location Questions
i. Where are the stores and customers locatd?
ii. Which stores are selling the most?
#### Plot Transactions per Stores
ggplot(Sales_Stores) +
aes(x = reorder(Store.Postcode,-N), y = N) +
geom_col(fill = "#1c6155") +
labs(x = "Stores",
y = "Number Of Transactions", title = "Number of Transactions per Store", subtitle = "In 2018") +
theme_classic() + geom_text(aes(label = N), vjust = 1.5, hjust=1.1, colour = "white", angle=90,size=3) + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

# Plot Revenues per Stores
ggplot(Sales_Stores_2) +
aes(x = reorder(Store.Postcode,-Revenues), y = Revenues) +
geom_col(fill = "#1c6155") +
labs(x = "Store Postcode",
y = "Revenues", title = "Revenues per Stores", subtitle = "In 2018") + geom_text(aes(label = Revenues), vjust = 1.5, hjust=1.1, colour = "white", angle=90,size=3) +
theme_classic()+theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

iii. How far stores are selling the most?
iv. How far stores are selling the most? - Alternative
c.Revenue Questions
i. How do the sales volume in each store relate to Acell’s
revenues?
ggplot(Sales_Stores_Revenues) +
aes(x = reorder(Store.Postcode,-Percentage_Revenue), y = Percentage_Revenue) +
geom_col(fill = "#1c6155") +
labs(x = "Store Postcode", y = "% of Total Revenues", title = "Revenues Contribution per Stores",
subtitle = "In 2018") +
theme_classic()+theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +geom_text(aes(label = round(Percentage_Revenue,1)), vjust = 1.5, hjust=1.1, colour = "white", angle=90,size=3)

ii. How does this relationship depend on the configuration?
d.Configuration Questions
i. What are the details of each configuration? How does this relate
to price?
ii. Do all stores sell all configurations?
ggplot(Stores_Details) +
aes(x = Configuration) +
geom_histogram(bins = 30L, fill = "#112446") +
labs(x = "Stores ",
y = "Configurations Count", title = "Each Configurations per Stores", subtitle = "In 2018") +
theme_classic() +
facet_wrap(vars(Store.Postcode), scales = "free")
